Surface-enhanced Raman spectroscopy is capable of precise differentiation between re-dyed hair samples

Scalp hairs are readily present at most crime scenes because an average person sheds around 100 hairs a day. Forensic experts analyze hair found at crime scenes to identify suspects involved in a crime. Many people color their hair on a regular basis. Therefore, confirmatory analysis of hair colorants can be extremely useful in forensic investigation of hair evidence. However, most currently available methods for analysis of hair colorants are invasive, destructive, or not reliable. Surface enhanced Raman spectroscopy (SERS) is a minimally invasive, fast, and highly accurate technique that can be used to identify colorants present on hair. SERS is based on 106–108 enhancement of Raman scattering from molecules present in the close proximity to noble metal nanostructures. In this study, we investigate the extent to which SERS can be used to reveal coloration history of hair. We found that SERS enables nearly 100% identification of dyes of different color if those were applied on hair in the sequential order. The same accuracy was observed for colorants of different brand and type. Furthermore, SERS was capable of revealing the order in which two colorants were applied on hair. Finally, we demonstrated that SERS could be used to reveal hair coloration history if two randomly selected dyes of different color, brand and type were used to color the hair. These findings facilitate the need for forensic experts to account for hair that has been redyed and can be identified against a library of the same colorant combinations.

Expanding upon this, we investigate the extent to which SERS can detect underlying dyes if the hair was re-colored afterwards. One can envision four different scenarios of hair coloration: (i) overlaying hair dyes with different colors, (ii) overlaying hair colorants of different brands, (iii) overlaying hair colorants of different types, and (iv) overlaying hair colorants that have different brand, type, and color. In the current study, we examine all these combinations using SERS couped to chemometrics to determine the accuracy with which SERS can be used to unravel coloration history of hair.

Results and discussion
SERS-based identification of overlying colorants with different color. We first examined the extent to which SERS can be used to differentiate the underlaying hair colorants of the same brand and type (semi-permanent or permanent) but different colors (blue and purple). For this, hair was first colored by Wella semi-permanent blue (WSBu) and re-colored by Wella semi-permanent purple (WSPu) (WSBuWSPu) that possessed very similar components to WSBu chemical composition. Next, we reversed the order of colorant application by first coloring hair with WSPu and then re-dying it with WSBu (WSPuWSBu). We acquired SERS spectra from all those four samples, Fig. 1 We observed only minor spectral differences that cannot be used for unambiguous differentiation between WSBu-WSPu and WSPuWSBu, as well as between SERS spectra acquired from the hair colored with one and two dyes. To overcome this limitation, we used PLS-DA to investigate the accuracy of differentiation between all four classes of SERS spectra.
Our results show that WSBuWSPu, WSPu and WSBu can be identified with 100% accuracy, whereas WSPu-WSBu can be predicted with 98% accuracy, Table 1. These results demonstrate that the application of two colorants creates a unique dye appearance on hair that is distinctly different from individual dyes used to color the hair ( Figure S1). Our results also show that SERS can be used to identify the order of dyes application on hair, Table 2.

SERS-based identification of overlying colorants of different brands.
We investigated the extent to which SERS can be used to differentiate the underlaying hair colorants of different brands. For this, we colored hair using Ion semi-permanent purple (ISPu) and then applied Wella semi-permanent purple (WSPu) on this hair (ISPuWSPu). We also reversed the order of dye application and first colored hair with WSPu and re-dyed this hair sample with ISPu (WSPuISPu). Next, we acquired SERS spectra from these hair samples, as well as from hair colored by WSPu and ISPu themselves, Fig. 2  www.nature.com/scientificreports/ We found that both WSPuISPu and ISPuWSPu do not exhibit equally intense signatures of WSPu and ISPu. Instead, SERS spectra acquired from hair with two dyes dominate by the spectroscopic signatures of WSPu with no regards whether this colorant was under-or overlaying. These results demonstrate that an application of two colorants that belonged to different dye brands on hair creates the unique dye appearance that is distinctly different from individual colorants used to dye hair ( Figure S2).
Utilization of PLS-DA enabled highly accurate identification of all four groups of SERS spectra, Tables 3 and 4. These results demonstrate that SERS can be used to identify coloration history of hair in regard to the brands of colorants used on hair. Our results also show that SERS can be used to identify the order of application of different brands on hair, Tables 3 and 4.

SERS-based identification of overlying colorants of different types.
All hair colorants can be divided into two classes: permanent and semi-permanent. Permanent colorants are based on phenyldiamines Table 1. Cross-validation matrix of SERS spectra acquired from hair with single and dual-dyes of different colors with TPR of SERS-based identification of hair colored with two dyes of different color.  We first colored hair with Ion semi-permanent blue dye (ISBu) and then applied Ion permanent blue dye (IPBu) on the same hair (ISBuIPBu). We also reversed application of these colorants on hair (IPBuISBu), as well as dyed hair with ISBu and IPBu alone. SERS spectra acquired from these four hair samples are shown in the SERS-based differentiation of hair dyes of different color, brand, and type. One may wonder whether SERS can be used to determine hair coloration history of two randomly selected dyes of different color, brand, and type. To answer this question, we first colored hair with Wella semi-permanent purple (WSPu) hair dye that was colored afterwards with L'Oréal permanent red (LPR) hair dye (WSPuLPR). We also altered the order of hair coloration by these two dyes and first colored hair with LPR then re-dying it afterwards with WSPu (LPRWSPu). We also colored hair with just LPR and WSPu. Next, we collected SERS spectra from WSPuLPR, LPRWSPu, LPR and WSPu, Fig. 4. We found that vibrational bands observed in the SERS spectrum of WSPuLPR and LPRWSPu primarily originated from WSPu with very little contribution of LPR ( Figure S4).
PLS-DA was able to identify SERS spectra collected from all four classes with nearly 100% accuracy. The same accuracy was observed for the binary model built for WSPuLPR and LPRWSPu, Tables 7 and 8. These results demonstrate that SERS can be used to unravel hair dying history in regard to the color, brand and type of the colorants used to dye hair.

Conclusion
Our results show that SERS is capable of unravelling coloration history of hair in regard to the dye color, brand, and type that was used to color hair. We also found that spectroscopic fingerprints of re-dyed hair largely represent one of the two dyes used to color hair. This can be explained by different Raman cross-section of colorants in such pairs of dyes. Thus, the colorant with larger Raman cross-section of dyes in it dominates in the SERS spectra acquired from hair with two colorants present on it. Therefore, application of chemometric analysis of spectra is required to reveal the information about the underlying hair colorant. One can expect that forensic application of the discussed above SERS-based approach will required a library of hair colorants with two and three individual colorants simultaneously present on hair to enable robust and reliable determination of hair coloration history. Table 3. Cross-validation matrix of SERS spectra acquired from hair with single and dual-dyes of different brands with TPR of SERS-based identification of hair colored with two dyes of different brands.

Materials and methods
Hair coloring procedure. Blonde hair that was never colored prior to the experiments was collected from a hair salon in College Station, Texas from de-identified individuals. Hair was used as received without any pretreatment or washing. It was cut in small ponytails of approximately the same density and tightened with elastics to minimize hair lost during dying and washing. Six total hair dyes were used to investigate the extent to which SERS could be used to determine the hair dying history, Table 9.  Table 5. Cross-validation matrix of SERS spectra acquired from hair with single and dual-dyes of different types of colorants with TPR of SERS-based identification of hair colored with two dyes of different types.     www.nature.com/scientificreports/ All semi-permanent dyes were allowed to process approximately 45 min, and permanent hair dye was processed for ~ 60 min according to instructions provided by colorant manufacturers.
Surfaced enhanced Raman spectroscopy. Each hair sample was coated with 5 µl of gold nanoparticles' suspension (AuNPs). AuNPs were made in the laboratory according to the procedure developed by Esparza and co-workers 12 . These spherical nanoparticles had ~ 80 nm in diameter. Prior to utilization on hair, the suspension of AuNPs was centrifuged at ~ 5000 g for 10 min to concentrate AuNPs. Next, the pellet of AuNPs was resuspended in DI water to remove detergent used for the nanoparticle synthesis. SERS spectra were acquired on a TE-2000U Nikon inverted confocal microscope equipped with a 20 × Nikon objective. The objective was used to focus the laser light (λ = 785 nm) generated by continuous wavelength laser on the sample. The same objective was used to collect scattered photons that were directed to a 50/50 light beam splitter and then passed to IsoPlane-320 spectrograph (Princeton Instruments) equipped with a 600 groove/mm grating. A long-pass filter (Semrock, LP-785RS-25) was used to cut off inelastically scattered photons. Laser power at the sample was ~ 1.8 mW. Spectral acquisition times were varying dependent on sample, but all were under 60 s. All reported SERS spectra were normalized and baselined. Spectral resolution was 2 cm −1 .

Data analysis.
We used Matlab equipped with Partial Least Squares Differentiative Analysis toolbox (Eigenvector Research Inc) for statistical analyses of the collected SERS spectra. All spectra were pre-processed by baselining using a second order automatic weighted least squares, taking the first derivative of spectral intensities with a second polynomial order and filter length of 15. SERS spectra were also area normalized and mean cantered. Partial least squared discriminant analysis (PLS-DA) was used to build all models 20,21 . Each model had 3-7 principal components. True positive rate (TPR) of the model performance is reported for each model in Tables 1, 2, 3 , 4, 5, 6, 7, 8, 9.

Data availability
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.